A commercial due-diligence checklist for business executives: 12 critical technical, legal, and operational questions to ask software vendors selling AI solutions.
Conducting rigorous vendor due diligence protects your business from data leaks, hidden token markups, and abandoned proprietary software.
Every business executive is currently inundated with sales pitches from software vendors promising to revolutionize their business with AI. However, venture capital data reveals that an estimated 75% of these solutions are "thin wrappers": software that does little more than take your prompt, forward it to OpenAI or Anthropic with a 500% price markup, and display the result in a basic UI. When that underlying LLM updates its interface or releases an identical native feature, these wrapper companies collapse overnight, leaving their business customers stranded. Before signing a multi-year software agreement, procurement teams must execute rigorous due diligence.
Question 1: Do you, or any of your upstream subprocessors, use our company data, prompts, or customer interactions to train your AI models? - Expected Answer: An unqualified "No". The vendor must show an explicit clause in their Data Processing Agreement (DPA) guaranteeing zero-retention or zero-training. Question 2: In which geographic jurisdictions is our data processed and stored? - Crucial for Australian and European businesses subject to the Privacy Act 1988 or GDPR. Data containing Australian citizen health or financial records must meet strict data residency requirements. Question 3: Who owns the intellectual property of the generated outputs, code, and synthesized databases? - Contract terms must explicitly state that the client retains 100% full legal title to all inputs, outputs, and derivative analytical models. Question 4: How is data isolated between different tenant accounts? - The vendor must prove logical or physical tenant isolation (such as vector database namespace partitioning) to guarantee your data cannot bleed into a competitor inquiry.
Question 5: Which specific underlying foundation models power your platform, and what is your multi-model redundancy strategy? - If the vendor relies entirely on a single model endpoint without fallback logic, an outage at OpenAI takes your entire commercial operation offline. Question 6: How do you measure and benchmark hallucination rates for our specific industry domain? - Demand concrete evaluation metrics (such as Ragas or TruLens scores) on industry-specific datasets, not generic marketing claims of "99% accuracy". Question 7: What programmatic guardrails and human-in-the-loop controls exist to prevent unauthorized actions? - Does the system allow automated financial transactions or refunds without human sign-off? Question 8: What are your guaranteed response latency and uptime Service Level Agreements (SLAs)? - An AI receptionist or customer chat tool with an average response time of 8 seconds will destroy your customer conversion rate.
Question 9: What is the exact pricing model: seat-based, flat monthly, or variable token/usage consumption? - Beware of hybrid models where you pay $500/month plus unpredictable per-token overages that balloon during busy holiday seasons. Question 10: Can we export our complete raw data, vector embeddings, and prompt configurations in an open format if we cancel? - If the vendor locks your knowledge base in a proprietary encrypted format that cannot be exported to standard JSON or CSV, you are effectively trapped. Question 11: Does your software adhere to open protocol standards like the Model Context Protocol (MCP)? - Open protocols ensure your integrations remain compatible with future AI tooling. Question 12: What is your financial runway, and what happens to our data and software access if your company is acquired or shuts down?
Assign a simple 1 to 5 score across each of the 12 questions. Any vendor that scores less than 45 out of 60, or fails to provide an unambiguous answer on Data Training Terms (Question 1) or Data Portability (Question 10), should be disqualified from enterprise consideration.
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